Senior GenAI/ML Specialist Solutions Architect

- Bachelor's degree in computer science, engineering, mathematics or equivalent, or experience in a professional field or military
- Experience in IT development or implementation/consulting in the software or Internet industries
- Experience within specific technology domain areas (e.g. software development, cloud computing, systems engineering, infrastructure, security, networking, data & analytics).
- Experience in design, implementation, or consulting in applications and infrastructures
- Experience communicating across technical and non-technical audiences, including executive level stakeholders or clients - Knowledge of SAP systems (like SAP Business Suite, S/4HANA, SAP Business Warehouse, SAP HANA, SAP Business Objects, etc.) and their architecture and infrastructure needs
- Experience in infrastructure architecture, database architecture and networking
- Experience working with end user or developer communities

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AWS is seeking a Senior GenAI/ML Specialist Solutions Architect (SSA) for large enterprise customers in United Kingdom of Great Britain and Ireland (UKI). You'll be instrumental in driving the adoption of AWS GenAI/ML services while helping organizations achieve their innovation and transformation goals through GenAI/ML architectures.

This role requires technologists with deep domain-specific expertise, able to address advanced concepts and feature designs. As part of the AGS organization, SSAs work with customers who have complex requirements that require expert-level knowledge to solve. You must have deep technical expertise in Generative AI and Machine Learning services, enterprise-grade architectural strategies, and a passion for solving complex business challenges. We are looking for technical experts who can bridge the gap between innovative AI technologies and practical business outcomes for our customers.

You'll work directly with a diverse top range of customers in UKI to help them make optimal technical decisions in their AI/ML journey, balancing innovation with practical constraints and compliance requirements. You'll have the opportunity to build enduring relationships with these organizations and establish yourself as a trusted technical advisor in the rapidly evolving field of Generative AI and Machine Learning.

Key job responsibilities
1. Technical Expertise & Solution Design: Provide customers with deep technical expertise in Generative AI and Machine Learning technologies, designing innovative solutions that help them meet strategic business objectives using AWS products

2. Customer Advisory: Act as a trusted advisor to line of business, AI, Data, and C-suite leaders, translating complex technical concepts into business value and guiding their AI transformation journey

3. Technical Leadership: Lead architectural reviews, workshops, and proof-of-concepts to advance customers' technical objectives and validate proposed solutions

4. Thought Leadership: Share best practices through AWS blogs, whitepapers, reference architectures, and helping to educate the broader technical community

5. Product Feedback & Innovation: Gather customer insights to inform AWS product teams about market needs and opportunities, contributing to the evolution of AWS GenAI/ML services based on real-world implementation experience

6. External Representation: Serve as a thought leader in the Generative AI space, representing AWS at industry events and conferences, such as AWS Summit and re:Invent

A day in the life
You bring hands-on experience designing and implementing machine learning solutions, including model training, deployment, and inference at scale, with working knowledge of AI/ML frameworks such as PyTorch, TensorFlow, Hugging Face, and scikit-learn. You have a background in a customer-facing technical role (solutions architect, ML engineer, data scientist, or technical consultant) and can communicate complex technical concepts to both technical and non-technical audiences, including C-suite executives. You hold domain expertise in one or more of: natural language processing, computer vision, generative AI, recommendation systems, or conversational AI.

Ideally, you also have experience with large language models (LLMs), foundation models, retrieval-augmented generation (RAG), fine-tuning, prompt engineering, or agentic AI architectures. Hands-on experience with AWS AI/ML services such as Amazon Bedrock, Amazon SageMaker, Amazon Q, or Amazon Rekognition is highly valued. Experience architecting end-to-end ML pipelines including data ingestion, feature engineering, model training, evaluation, and production deployment (MLOps) would further strengthen your candidacy.